Concept of the depth of anesthesia has been of interest for recent decades, and several measures have been proposed to assess the depth of anesthesia. Recently, however, this unitary anesthesia theory of the existence of one-dimensional concept called “depth of anesthesia” has been strongly criticized as oversimplified. Instead it has been suggested that the anesthesia has not one but three main components: hypnosis, analgesia and muscle relaxation. Different anesthetic regimens have different effect on these three components. Furthermore, they have effects on both cortical and sub-cortical levels. An adequate anesthesia means unresponsiveness to both noxious and non-noxious stimuli. The former may be defined by means of hemodynamic, motor and endocrine stability, while the latter is related to the loss of consciousness and recall and amnesia. In practice the adequate anesthesia is administered by using a combination of drugs with different effects on the brain autonomic nervous system and neuromuscular junction. The combination of these effects hence creates the hypnotic, analgesic and muscle relaxing effects.
In general anesthesia the patient is conducted through the phases of anesthesia from the induction to the varying lengths of maintenance period and to the final emergence out from anesthesia. Though the patient does not usually recall any surgical events or perceive surgical pain, the recovery and the post-operative comfort of the patient very often depend on the quality of the anesthesia during the operation itself. Adequate administration of analgesic drugs—meaning that over-doses and under-doses can be avoided during anesthesia—is believed to advance the recovery of the patient. It has been suggested that this is due to two main reasons. Surgical pain may sensitize the pain pathways during surgery and thus lower the pain threshold in such a way that even rather intense pain management in the post-operative period is ineffective. It is said that the best way to avoid post-operative pain is a good and adequate administration of analgesics during operation. The other mechanism is probably related to the secretions of stress hormones during surgery. These hormones may have their effects long after surgery and can slow down the physical and psycho-physiological heeling of the patient. Adequate pain medications can suppress the autonomic nervous system and prevent excess secretion of these stress hormones. In this context the term “nociception” is commonly used to refer to the perception of pain. The receptors involved in pain detection are aptly enough referred to as nociceptors. Nociceptive input is conveyed from the peripheral end organs to the central nervous system. Projection neurons in the spinal dorsal horn project to cell nuclei in supraspinal areas such as the thalamus, the brainstem, the midbrain etc. Of these, the synaptic junctions in the thalamus play a very important role in the integration and modulation of spinal nociceptive and non-nociceptive inputs. Nociceptive inputs are finally conducted to the cortex, where the sensation of pain is perceived. Stimulation of these central nervous system regions either electrically or chemically, e.g. by morphine and other opiates, produces analgesia in humans.
Currently the anesthesia practices rely on rather subjective assessments of the adequacy of the drug treatment during anesthesia. Anesthesiologists observe the patient and decide for the proper drugs they give to the patient. Though this often is enough to avoid adverse events such as arousal or muscle movements during surgery, which in fact very seldom occur in normal anesthesia nowadays, more objective measures for the anesthesia are needed. Recently the progress in the biopotential signal analysis has lead in reasonable quantitative estimation of the hypnotic level of the patient, and thereby the titration of the anesthetic agents can be guided by these new measurements.
For instance, the neurological activity of the brain is reflected in biopotentials available on the surface of the brain and on the scalp. Thus, efforts to quantify the extent of anesthesia-induced hypnosis have turned to a study of these biopotentials. The biopotential electrical signals are usually obtained by a pair, or plurality of pairs, of electrodes placed on the patients scalp at locations designated by a recognized protocol and a set, or a plurality of sets or channels, of electrical signals are obtained from the electrodes. These signals are amplified and filtered. The recorded signals comprise an electroencephalogram or EEG, which normally has no obvious repetitive patterns, contrary to other biopotential signals, like electrocardiogram (ECG). Among the purposes of filtering is to remove electromyographic (EMG) signals from the EEG signal. EMG signals result from muscle activity of the patient and will appear in electroencephalographic electrodes applied to the forehead or scalp of the patient. They are usually considered artifacts with respect to the EEG signals. Since EMG signals characteristically have most of their energy in a frequency range from 40 Hz to 300 Hz, which is different than that of the EEG, major portions of the EMG signals can be separated from the contaminated EEG signal.
A macro characteristic of EEG signal patterns is the existence of broadly defined low frequency rhythms or waves occurring in certain frequency bands. Four such bands are recognized: Alpha waves are found during periods of wakefulness and may disappear entirely during sleep. The higher frequency Beta waves are recorded during periods of intense activation of the central nervous system. The lower frequency Theta and Delta waves reflect drowsiness and periods of deep sleep.
For clinical use, it is desirable to simplify the results of EEG signal analysis of the foregoing, and other types, into a workable parameter that can be used by an anesthesiologist in a clinical setting when attending the patient. Various such parameters for relating EEG signal data to the hypnotic state of the patient are discussed in the literature. Several use frequency domain power spectral analysis. These parameters include peak power frequency (PPF), median power frequency (MPF), and spectral edge frequency (SEF). A peak power frequency (PPF) parameter uses the frequency in a spectrum at which occurs the highest power in the sampled data as an indication of the depth of anesthesia. The median power frequency (MPF) parameter, as its name implies, uses the frequency that bisects the spectrum. In the same fashion, the spectral edge frequency uses the highest frequency in the EEG signal. To improve the consistency of an indicator of the hypnotic state or depth of anesthesia, several parameters are often employed in combination. For example, the spectral edge frequency (SEF) parameter may be combined with the time-domain burst suppression ratio (BSR) parameter to improve the consistency and accuracy with which the depth of anesthesia can be indicated. Also more complex combinations of parameters, like bispectral index (BIS) have been described.
There are a number of concepts and analytical techniques directed to the complex nature of random and unpredictable signals, like EEG. One such concept is entropy. Entropy, as a physical concept, describes the state of disorder of a physical system. Applying the concept of entropy to the brain, the premise is that when a person is awake, the mind is full of activity and hence the state of the brain is more nonlinear, complex, and noise like. Since EEG signals reflect the underlying state of brain activity, this is reflected in relatively more “randomness” or “complexity” in the EEG signal data, or, conversely, in a low level of “order.” As a person falls asleep or is anesthetized, the brain function begins to lessen and becomes more orderly and regular. As the activity state of the brain changes, this is reflected in the EEG signals by a relative lowering of the “randomness” or “complexity” of the EEG signal data or conversely, increasing “order” in the signal data. When a person is awake, the EEG data signals will have higher entropy and when the person is asleep the EEG signal data will have a lower entropy.
According to the International Publication WO-02/32305 both the EEG and EMG signal data are typically obtained from the same set of electrodes applied, for example, to the forehead of the patient. The EEG signal component dominates the lower frequencies (up to about 30 Hz) contained in the biopotentials existing in the electrodes and EMG signal component dominates the higher frequencies (about 50 Hz and above). The presence of EMG signal can provide a rapid indication of the conscious-unconscious state of the patient. Importantly, because of the higher frequency of the EMG data signal, the sampling time can be significantly shorter than that required for the lower frequency EEG signal data. This allows the EMG data to be computed more frequently so that the overall diagnostic indicator can quickly indicate changes in the state of the patient. In one embodiment of the publication, the EEG signal data and the EMG signal data are separately analyzed and thereafter combined into a diagnostic index or indicator. As noted above, because of the celerity with which changes in the anesthetic state of the patient can be determined from the EMG data, the overall index can quickly inform the anesthesiologist of changes in the state of the patient.
Another way is to observe a photoplethysmographic (=PPG) signal, which is obtained by measuring the intensity of light transmitted through or reflected by the tissue. The dynamic part of the signal is caused by variations in blood volume and perfusion of the tissue, affecting scattering and absorption of the incident light. The most usual application of the signal is the measurement of the oxygen saturation of blood. The pulse waveform of the PPG signal is closely similar to that of the intra-arterial blood pressure. The waveform is reflecting the interaction between left ventricular output, i.e. cardiac output or stroke volume, and the capacitance of the vascular tree, also called vascular resistance. Blood pressure is determined by the cardiac output, which is stroke volume multiplied by heart rate, and vascular resistance. However, in addition to these global circulatory parameters, the dynamic capacitance of the vasculature affects also the nonlinear relationship of PPG signal and circulatory parameters. Especially complex is the relationship between the PPG waveform shape within one pulse and the integrated pulse-to-pulse variables. The PPG signal is related to the changes in peripheral tissue blood volume and blood absorptivity. As it is the blood flow, that causes the blood volume changes, the PPG signal is hence indirectly related to local blood flow. The flow, in turn, depends on the pressure gradient and local vascular dynamic resistance and capacitance.
The PPG measuring as such has been utilized for a long time. For instance U.S. Pat. No. 6,117,075 discloses a method and device for monitoring the depth of anesthesia (=DOA) during surgery by analyzing patterns and characteristics of oscillatory phenomena in measured pulse pressure and skin temperature signals. The method utilizes pulse pressure and skin temperature oscillatory patterns describe the nature of sympathetic vasomotor tone. The method monitors DOA in two ways. Spectral characteristics of skin temperature or pulse pressure oscillatory phenomena are used to describe the depth of anesthesia, and the concordance between oscillatory patterns of two physiological signals, which have been recorded simultaneously but at different locations, are used to describe the depth of anesthesia. According to the publication a PPG signal of an anesthetized patient is continuously monitored, and the recorded raw PPG signal is then processed so as to generate a signal depicting the beat-to-beat pulse pressure amplitude. Then the signal is derived by detecting peaks, and calculating the difference between each positive-negative peak pair, after which a further signal is processed in a manner so as to derive a data set describing very low frequency variations in pulse pressure over time in the 0.01-0.04 Hz range, that is, the PPG signal amplitude variability. Power spectrum analysis is finally performed on said further signal, and the received frequency power spectrum characteristics are used to describe the DOA, such that a progressively narrower bandwidth describe a progressively deeper level of anesthesia.
However, it appears that the position of the dicrotic notch as well as the PPG amplitude are dependent on various other sources than the status of vasoconstriction or vasodilatation, including fluid balance, temperature of the site of PPG measurement, heart rate, etc. Hence, these parameters may be interpreted with caution. Furthermore, they refer to the usage of the PPG information as a measure of the depth of anesthesia, which is an oversimplified one-dimensional assumption as described above.
The publication E. Seitsonen, M. van Gils, I. Korhonen, K. Korttila, A. Yli-Hankala: “EEG, Heart Rate, Pulse Plethysmography and Movement Responses to Skin Incision”—A-582, 2002 ASA Meeting Abstracts, Oct. 16, 2002, discloses studies concerning evaluation of analgesia and nociception during general anesthesia. Raw EEG, the bispectral index, electrocardiography (ECG) and PPG data were collected and analyzed offline. RR-interval (RRI) tachogram was derived from ECG and frontal electromyography (FEMG) from EEG, and several beat-to-beat morphology parameters were derived from PPG signal, as well as various time and frequency domain parameters were computed from EEG, RRI and PPG data. When derived variables calculated as ratios or differences between post-incision and pre-incision values were compared, RRI, amplitude of the dicrotic notch in PPG, EEG spectral entropy and FEMG power appeared as primary variables in the optimal linear discriminant function between movers and non-movers. Finally it was concluded that combination of these parameters may be useful in assessing the level of analgesia and nociception during anesthesia. However, the publication does not provide any practical procedure for monitoring a patient.
U.S. Pat. No. 6,338,713, discloses a system and method for providing information to the user of a medical monitoring or diagnostic device to aid in the clinical decision making process. The preferred embodiment uses two estimators or predictors of the same physiological quantity, with each of the estimators being designed to detect specific states or artifacts in the estimated parameter and thus operating at a different point on its respective ROC curve; one chosen to provide high sensitivity, the other chosen to provide high specificity. The divergence between the estimators is indicated by the use of a shaded region between their respective time trends. The use of two estimators of the same parameters with different performance characteristics allows the system and method of the present invention to derive additional information about the underlying physiologic process over and above that which would be available from a single estimator. The system and method of the present invention can derive information from not only the instantaneous values of the estimators and the difference between them, but also from the time trend of the difference. Accordingly, this publication is primarily directed to the accuracy of a measurement, but does not discuss the problem how the level of analgesia could be reliably monitored while the patient is under anesthesia or sedation.
Anyway, the administration of analgetics is still largely based on the visual observations of the vital signs and the hemodynamic responses of the patient to surgical stimulation. Analgesic drugs are usually given, when the heart rate or blood pressure show fast increases or are in long term at the high end of the normal ranges. Different motoric responses, sweating and lacrimation of the patient can be observed as well. A further problem not considered in the publications is the possible suppressive effect of relaxants on at least some signals acquired from patient, which can have adverse effect on the reliability of the results received. The concept of analgesia is very complex and due to inter- and intraindividual variability the combined specificity and sensitivity for the single-parameter based methods is not very good.
Accordingly the main object of the invention is to achieve a method and apparatus for monitoring the anesthesia or sedation of a patient so that a reliable data about level or depth of analgesia would be available to an anesthetist or to other purposes.
The second object of the invention is to achieve a method and apparatus for monitoring the anesthesia or sedation capable of using measured signals derived from various sources of the patient, which means that the method should not be dependent on any single type of detector.
The third object of the invention is to achieve a method and apparatus for monitoring the anesthesia or sedation capable to deliver such results as an output, with the basis of which the adequacy of analgesia could be reliably enough assessed by inexperienced anesthetists or other operators, too.
The fourth object of the invention is to achieve a method and apparatus for monitoring ing the anesthesia or sedation functioning with an acceptable speed so that a change in analgesia to a hazardous direction is detected and reported early enough to allow timely corrective actions.